{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from tqdm import tqdm_notebook\n",
    "from easydict import EasyDict as edict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = edict()\n",
    "DATA.grid = edict()\n",
    "DATA.grid.shape = (7, 10)\n",
    "DATA.grid.startPoint = (3, 0)\n",
    "DATA.grid.endPoint = (3, 7)\n",
    "DATA.grid.wind = (0 ,0, 0, 1, 1, 1, 2, 2, 1, 0)\n",
    "DATA.actionSet = [(0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (1, -1), (-1, -1), (-1, 1), (0, 0)]\n",
    "DATA.episode = edict({'S':[], 'A':[], 'R':[]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_ACTION = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "def getPossiableActions(state, num_action):\n",
    "    if LEGAL_ACTION[state[0]][state[1]] != []:\n",
    "        return LEGAL_ACTION[state[0]][state[1]]\n",
    "    else:\n",
    "        for i in range(num_action):\n",
    "            if state[0] + DATA.actionSet[i][0] <= 6 and state[0] + DATA.actionSet[i][0] >= 0 and state[1] + DATA.actionSet[i][1] <= 9 and state[1] + DATA.actionSet[i][1] >= 0:\n",
    "                LEGAL_ACTION[state[0]][state[1]].append(DATA.actionSet[i])\n",
    "        return LEGAL_ACTION[state[0]][state[1]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def actionMapTo2D(num):\n",
    "    return DATA.actionSet[num]\n",
    "\n",
    "def actionMapTo1D(action2D, num_action):\n",
    "    for i in range(num_action):\n",
    "        if action2D == DATA.actionSet[i]:\n",
    "            return i\n",
    "\n",
    "def epislonGreedy(epislon, state, legalAction, Q, num_action):\n",
    "    rd = np.random.uniform(0, 1)\n",
    "    actionLen = len(legalAction)\n",
    "    choices = [-1, 0, 1]\n",
    "    randomMove = np.random.choice(choices)\n",
    "    if rd < 1 - epislon:\n",
    "        maxAction = legalAction[0]\n",
    "        maxAction1D = actionMapTo1D(maxAction, num_action)\n",
    "        maxStateAtferAction = np.array([min(max(state[0] + legalAction[0][0] - DATA.grid.wind[state[1]] + randomMove, 0), 6), state[1] + legalAction[0][1]], dtype = int)\n",
    "        maxActionValue = Q[state[0]][state[1]][maxAction1D]\n",
    "        for i in range(1, actionLen):\n",
    "            action1D = actionMapTo1D(legalAction[i], num_action)\n",
    "            tmpState = np.array([min(max(state[0] + legalAction[i][0] - DATA.grid.wind[state[1]] + randomMove, 0), 6), state[1] + legalAction[i][1]], dtype = int)\n",
    "            if Q[state[0]][state[1]][action1D] > maxActionValue:\n",
    "                maxActionValue = Q[state[0]][state[1]][action1D]\n",
    "                maxAction = legalAction[i]\n",
    "                maxStateAtferAction = tmpState\n",
    "        return maxAction, maxStateAtferAction\n",
    "    else:\n",
    "        rd2 = np.random.randint(0, actionLen)\n",
    "        returnState = np.array([min(max(state[0] + legalAction[rd2][0] - DATA.grid.wind[state[1]] + randomMove, 0), 6), state[1] + legalAction[rd2][1]], dtype = int)\n",
    "        return legalAction[rd2], returnState\n",
    "\n",
    "\n",
    "def sarsa(n_episode, gamma, epislon, alpha, num_action):\n",
    "    Q = np.zeros(shape = (7, 10, num_action))\n",
    "    endState = np.array(DATA.grid.endPoint, dtype = int)\n",
    "    stepCounter = 0\n",
    "    y = []\n",
    "    x = []\n",
    "    for episode in tqdm_notebook(range(n_episode)):\n",
    "        y.append(episode)\n",
    "        state = np.array(DATA.grid.startPoint, dtype = int)\n",
    "        legalAction = getPossiableActions(state, num_action)\n",
    "        action, newState = epislonGreedy(epislon, state, legalAction, Q, num_action)\n",
    "        while (state == endState).all() == False:\n",
    "            # print(state)\n",
    "            stepCounter += 1\n",
    "            action1D = actionMapTo1D(action, num_action)\n",
    "            legalActionNewState = getPossiableActions(newState, num_action)\n",
    "            actionNewState, newNewState = epislonGreedy(epislon, newState, legalActionNewState, Q, num_action)\n",
    "            actionNewState1D = actionMapTo1D(actionNewState, num_action)\n",
    "            Q[state[0]][state[1]][action1D] = Q[state[0]][state[1]][action1D] + alpha * (-1 + gamma * Q[newState[0]][newState[1]][actionNewState1D] - Q[state[0]][state[1]][action1D])\n",
    "            state = newState\n",
    "            action = actionNewState\n",
    "            newState = newNewState\n",
    "        stepCounter += 1\n",
    "        x.append(stepCounter)\n",
    "    np.save(\"Q\" + str(num_action) + \".npy\", Q)\n",
    "    return y, x, Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\inspur\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\ipykernel_launcher.py:39: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c7a845a2bf449469d97a5ea1fbfa587",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3de2849da6ce476bbffca484ca153b78",
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      "text/plain": [
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    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29372f5bd88>"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_episode = 200\n",
    "gamma = 1\n",
    "alpha = 0.5\n",
    "epislon = 0.1\n",
    "\n",
    "y8, x8, Q8 = sarsa(n_episode, gamma, epislon, alpha, 8)\n",
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "y9, x9, Q9 = sarsa(n_episode, gamma, epislon, alpha, 9)\n",
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "y4, x4, Q4 = sarsa(n_episode, gamma, epislon, alpha, 4)\n",
    "x8_per_eposide = [x8[0]]\n",
    "for i in range(1, len(x8)):\n",
    "    x8_per_eposide.append(x8[i] - x8[i-1])\n",
    "plt.plot(y8, x8_per_eposide, label = \"8 Actions\")\n",
    "x9_per_eposide = [x9[0]]\n",
    "for i in range(1, len(x9)):\n",
    "    x9_per_eposide.append(x9[i] - x9[i-1])\n",
    "plt.plot(y9, x9_per_eposide, label = \"9 Actions\")\n",
    "x4_per_eposide = [x4[0]]\n",
    "for i in range(1, len(x4)):\n",
    "    x4_per_eposide.append(x4[i] - x4[i-1])\n",
    "plt.plot(y4, x4_per_eposide, label = \"4 Actions\")\n",
    "plt.ylabel(\"Time Step\")\n",
    "plt.xlabel(\"Episode\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\inspur\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\ipykernel_launcher.py:39: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9cf437b68db4b2394c9fe636a0559d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8901c5fb990545dc86b2eb6a4b67750b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba89ad449d614c3a93cdf5557b6d9ac0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_episode = 5000\n",
    "gamma = 1\n",
    "alpha = 0.5\n",
    "epislon = 0.1\n",
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "y8, x8, Q8 = sarsa(n_episode, gamma, epislon, alpha, 8)\n",
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "y9, x9, Q9 = sarsa(n_episode, gamma, epislon, alpha, 9)\n",
    "LEGAL_ACTION = [[[] for j in range(10)] for i in range(7)]\n",
    "y4, x4, Q4 = sarsa(n_episode, gamma, epislon, alpha, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29373005608>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x8, y8, label = \"8 Actions\")\n",
    "plt.plot(x9, y9, label = \"9 Actions\")\n",
    "plt.plot(x4, y4, label = \"4 Actions\")\n",
    "plt.xlabel(\"Time Step\")\n",
    "plt.ylabel(\"Episode\")\n",
    "plt.legend()"
   ]
  }
 ],
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